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   "execution_count": 2,
   "id": "de8226fe-a3a0-4871-b87f-5f068c1f84ee",
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   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33875edb-d101-4e7f-8225-a078c86d14e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import exists\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import copy\n",
    "from fastprogress import progress_bar\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "\n",
    "from jsputils import classes\n",
    "\n",
    "import sys\n",
    "sys.path.append('/home/jovyan/work/DropboxSandbox/GSN')\n",
    "import gsn\n",
    "from gsn.rsa_noise_ceiling import rsa_noise_ceiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82b5b889-cfe9-4ab0-807c-7bd391ee79a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "savedir = f'{os.getcwd()}/analysis_outputs/3c-NoiseCeilings'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f137876b-8c68-4336-b782-9f4771c20632",
   "metadata": {},
   "outputs": [],
   "source": [
    "subjs = [f'subj0{s}' for s in range(1,9)]\n",
    "roi_list = ['FFA-1','FFA-2','OFA',\n",
    "            'PPA','OPA','EBA','FBA-1','FBA-2',\n",
    "            'VWFA-1','VWFA-2','OWFA']#,'PPA','EBA','VWFA-1']\n",
    "ncsnr_threshold = 0.3\n",
    "\n",
    "train_imageset = 'nonshared1000-3rep-batch0'\n",
    "val_imageset = 'nonshared1000-3rep-batch1'\n",
    "test_imageset = 'special515'\n",
    "\n",
    "space = 'nativesurface'\n",
    "beta_version = 'betas_fithrf_GLMdenoise_RR'\n",
    "ncsnr_threshold = 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3cdae1b6-ad86-4640-ac8d-dc1b8ecbbf39",
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   "source": [
    "def compute_gsn_noise_ceiling(data):\n",
    "    \n",
    "    threshold = 1000\n",
    "    n_sims = 3\n",
    "    nvox_per_sample = 1000\n",
    "    \n",
    "    data_gsn = np.transpose(copy.deepcopy(data), (2,0,1))\n",
    "    \n",
    "    rdmfuns = [lambda x: np.mean(x.T, axis = 1),\n",
    "               lambda x: pdist(x.T,'correlation')]\n",
    "    \n",
    "    out = dict()\n",
    "    \n",
    "    ###########\n",
    "    \n",
    "    if data_gsn.shape[0] > threshold:\n",
    "        \n",
    "        out['ncs'] = []\n",
    "        out['ncdists'] = []\n",
    "        out['results'] = []\n",
    "        \n",
    "        for n in range(n_sims):\n",
    "            \n",
    "            # get indices of k random voxels from the ROI\n",
    "            idx = np.random.choice(np.arange(data_gsn.shape[0]), nvox_per_sample)\n",
    "            \n",
    "            ncs, ncdists, results = rsa_noise_ceiling(data_gsn[idx], \n",
    "                                                     rdmfuns = rdmfuns,\n",
    "                                                     wantverbose=False)\n",
    "            \n",
    "            out['ncs'].append(ncs)\n",
    "            out['ncdists'].append(ncdists)\n",
    "            out['results'].append(results)\n",
    "            print(ncs)\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        ncs, ncdists, results = rsa_noise_ceiling(data_gsn, \n",
    "                                                  rdmfuns = rdmfuns,\n",
    "                                                  wantverbose=False)\n",
    "        \n",
    "        out['ncs'] = [ncs]\n",
    "        out['ncdists'] = [ncdists]\n",
    "        out['results'] = [results]\n",
    "        print(ncs)\n",
    "    \n",
    "    return out\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "overwrite = False\n",
    "\n",
    "for subj in progress_bar(subjs):\n",
    "    \n",
    "    NSDsubj = classes.fMRISubject(subj, space, beta_version)\n",
    "\n",
    "    for roi in progress_bar(roi_list):\n",
    "        \n",
    "        print(roi, subj)\n",
    "        \n",
    "        savefn = f'{savedir}/GSN-NC_{roi}_{subj}_{test_imageset}_nc-{ncsnr_threshold}.npy'\n",
    "        \n",
    "        if exists(savefn) and overwrite is False: \n",
    "            \n",
    "            print('skipping')\n",
    "            \n",
    "        else:\n",
    "\n",
    "            ROI = classes.BrainRegion(NSDsubj, roi)\n",
    "            ROI.load_betas()\n",
    "            ROI.get_ncsnr_mask(threshold = ncsnr_threshold)\n",
    "            ROI.load_encoding_data(train_imageset, val_imageset, test_imageset)\n",
    "            encoder = classes.EncodingProcedure(ROI, DNN, \n",
    "                                         method = 'lasso', \n",
    "                                         positive = True,\n",
    "                                         alphas = [0.1]) \n",
    "\n",
    "            y = encoder.get_encoding_voxels(mean = False)\n",
    "            \n",
    "            print(y['test'].shape)\n",
    "            if y['test'].shape[2] > 2:\n",
    "                NC = compute_gsn_noise_ceiling(y['test'])\n",
    "                np.save(savefn, NC, allow_pickle=True)\n",
    "\n",
    "            else:\n",
    "                print('skipping, insufficient voxels')"
   ]
  },
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       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8' class='' max='8' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8/8 00:00&lt;00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8' class='' max='8' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8/8 00:00&lt;00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = []\n",
    "\n",
    "for roi in progress_bar(roi_list):\n",
    "    for subj in progress_bar(subjs):\n",
    "        #print(roi, subj)\n",
    "        \n",
    "        savefn = f'{savedir}/GSN-NC_{roi}_{subj}_{test_imageset}_nc-{ncsnr_threshold}.npy'\n",
    "        \n",
    "        if exists(savefn):\n",
    "            nc = np.load(savefn, allow_pickle=True).item()\n",
    "            \n",
    "            if len(nc['ncs']) > 1:\n",
    "                this_ncs = np.mean(np.vstack(nc['ncs']),axis=0)\n",
    "            else:\n",
    "                this_ncs = np.array(nc['ncs'][0])\n",
    "                \n",
    "            #print(this_ncs)\n",
    "            \n",
    "            data.append([roi, subj, this_ncs[0], this_ncs[1]])\n",
    "            \n",
    "        else:\n",
    "            print(savefn, 'does not exist')\n",
    "\n",
    "df = pd.DataFrame(data, columns=['ROI', 'Subject', 'Univariate', 'RSA'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3a030fdf-9f39-4d29-ba9e-98207dda8c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Custom Palette\n",
    "palette = ['tomato']*3 + ['limegreen']*2 + ['dodgerblue']*3 + ['purple']*3\n",
    "\n",
    "\n",
    "# Calculate unique ROIs and subjects\n",
    "unique_rois = df['ROI'].unique()\n",
    "unique_subjs = df['Subject'].unique()\n",
    "\n",
    "# Create subplots\n",
    "fig, ax = plt.subplots(2, 1, figsize=(16, 10))\n",
    "\n",
    "# Marker size\n",
    "marker_size = (0.12, 0.02)\n",
    "\n",
    "# Metrics to plot\n",
    "metrics = ['Univariate', 'RSA']\n",
    "\n",
    "# Loop over each metric\n",
    "for i, metric in enumerate(metrics):\n",
    "    # Loop over each ROI\n",
    "    for j, roi in enumerate(unique_rois):\n",
    "        # Get the data for this ROI\n",
    "        roi_data = df[df['ROI'] == roi]\n",
    "\n",
    "        # Loop over each subject\n",
    "        for k, subj in enumerate(unique_subjs):\n",
    "            # Get the data for this subject\n",
    "            subj_data = roi_data[roi_data['Subject'] == subj]\n",
    "\n",
    "            # If there is no data for this subject in this ROI, skip this iteration\n",
    "            if subj_data.empty:\n",
    "                continue\n",
    "\n",
    "            # Plot the data\n",
    "            rect = Rectangle((j + k*0.1 - marker_size[0]/2, subj_data[metric].tolist()[0] - marker_size[1]/2),\n",
    "                             marker_size[0], marker_size[1], color=palette[j])\n",
    "            ax[i].add_patch(rect)\n",
    "\n",
    "    # Set the x-ticks at the center of each group of subjects\n",
    "    ax[i].set_xticks([j + (len(unique_subjs)-1)*0.05 for j in range(len(unique_rois))])\n",
    "\n",
    "    # Set the x-tick labels to be the ROIs\n",
    "    ax[i].set_xticklabels(unique_rois)\n",
    "\n",
    "    # Set the titles and labels\n",
    "    ax[i].set_title(f'{metric} Noise Ceiling')\n",
    "    ax[i].set_xlabel('ROI')\n",
    "    ax[i].set_ylabel('Noise Ceiling (r)')\n",
    "\n",
    "    # Set the y-axis limits\n",
    "    ax[i].set_ylim([0, 1])\n",
    "\n",
    "    # Set the x-axis limits\n",
    "    ax[i].set_xlim([-0.5, len(unique_rois)])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bda65676-a5a9-4d9e-8619-3de1035e3e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Custom Palette\n",
    "palette = ['tomato']*3 + ['limegreen']*2 + ['dodgerblue']*3 + ['purple']*3\n",
    "\n",
    "# Calculate unique ROIs and subjects\n",
    "unique_rois = df['ROI'].unique()\n",
    "unique_subjs = df['Subject'].unique()\n",
    "\n",
    "# Create subplots\n",
    "fig, ax = plt.subplots(2, 1, figsize=(20, 12))\n",
    "\n",
    "# Metrics to plot\n",
    "metrics = ['Univariate', 'RSA']\n",
    "\n",
    "# Space between ROIs\n",
    "space = 0.3\n",
    "\n",
    "# Loop over each metric\n",
    "for i, metric in enumerate(metrics):\n",
    "    # Create secondary x-axis for ROIs\n",
    "    ax2 = ax[i].twiny()\n",
    "\n",
    "    # Loop over each ROI\n",
    "    for j, roi in enumerate(unique_rois):\n",
    "        # Get the data for this ROI\n",
    "        roi_data = df[df['ROI'] == roi]\n",
    "\n",
    "        # Loop over each subject\n",
    "        for k, subj in enumerate(unique_subjs):\n",
    "            # Get the data for this subject\n",
    "            subj_data = roi_data[roi_data['Subject'] == subj]\n",
    "\n",
    "            # If there is no data for this subject in this ROI, skip this iteration\n",
    "            if subj_data.empty:\n",
    "                continue\n",
    "\n",
    "            # Calculate the x-position\n",
    "            x_pos = j * (1 + space) + k * 0.1\n",
    "\n",
    "            # Plot the data with dots instead of rectangles\n",
    "            ax[i].scatter(x_pos, subj_data[metric].tolist()[0], color=palette[j], s=30)\n",
    "\n",
    "            # Add a stem (vertical line) going up to the dot\n",
    "            ax[i].vlines(x_pos, 0, subj_data[metric].tolist()[0], color=palette[j], linestyle='dashed')\n",
    "\n",
    "    # Set the x-ticks at the center of each group of subjects\n",
    "    ax[i].set_xticks([(j * (1 + space)) + k * 0.1 for j in range(len(unique_rois)) for k in range(len(unique_subjs))])\n",
    "\n",
    "    # Set the x-tick labels to be the subjects\n",
    "    ax[i].set_xticklabels([f'subj{str(k+1).zfill(2)}' for j in range(len(unique_rois)) for k in range(len(unique_subjs))], rotation='vertical', fontsize='small')\n",
    "\n",
    "    # Set the secondary x-ticks and labels to be the ROIs\n",
    "    ax2.set_xticks([(j * (1 + space)) + ((len(unique_subjs) - 1) * 0.05) for j in range(len(unique_rois))])\n",
    "    ax2.set_xticklabels(unique_rois,fontsize=14)\n",
    "\n",
    "    # Set the titles and labels\n",
    "    ax[i].set_title(f'{metric} Noise Ceiling',fontsize=20)\n",
    "    #ax[i].set_xlabel('Subject')\n",
    "    #ax2.set_xlabel('ROI')  # The ROI label is now on the secondary x-axis\n",
    "    ax[i].set_ylabel('Noise Ceiling (r)',fontsize=14)\n",
    "\n",
    "    # Set the y-axis limits\n",
    "    ax[i].set_ylim([0, 1])\n",
    "\n",
    "    # Set the x-axis limits\n",
    "    ax[i].set_xlim([-0.5, len(unique_rois) * (1 + space)])\n",
    "    ax2.set_xlim(ax[i].get_xlim())  # Set the secondary x-axis limits to be the same as the primary x-axis\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dnffa",
   "language": "python",
   "name": "dnffa"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
